IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization

Feida Zhu, Yongfeng Zhang, Neil Yorke-Smith, Guibing Guo, Xu Chen

Research output: Contribution to conferenceAbstractScientific

3 Citations (Scopus)
21 Downloads (Pure)

Abstract

Recommendation system has became an important component in many real applications, ranging from e-commerce, music app to video-sharing site and on-line book store. The key of a successful recommendation system lies in the accurate user/item profiling. With the advent of web 2.0, quite a lot of multimodal information has been accumulated, which provides us with the opportunity to profile users in a more comprehensive manner. However, directly integrating multimodal information into recommendation system is not a trivial task, because they may be either homogenous or heterogeneous, which requires more advanced method for both fusion and alignment. This workshop aims to provide a platform for discussing the challenges and corresponding innovative approaches in fusing multi-dimensional information for user modeling and recommender systems. We hope more advanced technologies can be proposed or inspired, and also we hope that the direction of integrating different types of information can catch much more attention in both academic and industry.

Original languageEnglish
Pages804-805
Number of pages2
DOIs
Publication statusPublished - 2018
EventWSDM 2018: 11the ACM International Conference on Web Search and Data Mining - Los Angeles, CA, United States
Duration: 6 Feb 20189 Feb 2018
Conference number: 11
http://www.wsdm-conference.org/2018/

Conference

ConferenceWSDM 2018
CountryUnited States
CityLos Angeles, CA
Period6/02/189/02/18
Internet address

Keywords

  • Information fusion
  • Multi-dimensional
  • User modeling

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    Zhu, F., Zhang, Y., Yorke-Smith, N., Guo, G., & Chen, X. (2018). IFUP: Workshop on Multi-dimensional Information Fusion for User Modeling and Personalization. 804-805. Abstract from WSDM 2018, Los Angeles, CA, United States. https://doi.org/10.1145/3159652.3160592